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arXiv · 2106.07608

Recursive Refinement Network for Deformable Lung Registration between Exhale and Inhale CT Scans

Xinzi He·Jia Guo·Xuzhe Zhang·Hanwen Bi·Sarah Gerard·David Kaczka·Amin Motahari·Eric Hoffman·Joseph Reinhardt·R. Graham Barr·Elsa Angelini·Andrew Laine
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Abstract

Unsupervised learning-based medical image registration approaches have witnessed rapid development in recent years. We propose to revisit a commonly ignored while simple and well-established principle: recursive refinement of deformation vector fields across scales. We introduce a recursive refinement network (RRN) for unsupervised medical image registration, to extract multi-scale features, construct normalized local cost correlation volume and recursively refine volumetric deformation vector fields. RRN achieves state of the art performance for 3D registration of expiratory-inspiratory pairs of CT lung scans. On DirLab COPDGene dataset, RRN returns an average Target Registration Error (TRE) of 0.83 mm, which corresponds to a 13% error reduction from the best result presented in the leaderboard. In addition to comparison with conventional methods, RRN leads to 89% error reduction compared to deep-learning-based peer approaches.

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Xinzi He, Jia Guo, Xuzhe Zhang, Hanwen Bi, Sarah Gerard, David Kaczka, Amin Motahari, Eric Hoffman, Joseph Reinhardt, R. Graham Barr, Elsa Angelini, Andrew Laine. 2021-06-14. Recursive Refinement Network for Deformable Lung Registration between Exhale and Inhale CT Scans. https://arxiv.org/abs/2106.07608

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